公开(公告)号：US20160253672A1

公开(公告)日：2016-09-01

申请号：US14726353

申请日：2015-05-29

Applicant: Palantir Technologies, Inc.

Inventor： Sean Hunter ,  Samuel Rogerson ,  Anirvan Mukherjee

IPC: G06Q20/40 ,  H04L29/08

CPC classification number: G06Q20/4016 ,  G06Q40/06 ,  H04L67/10

Abstract: A computer system implements a risk model for detecting outliers in a large plurality of transaction data, which can encompass millions or billions of transactions in some instances. The computing system comprises a non-transitory computer readable storage medium storing program instructions for execution by a computer processor in order to cause the computing system to receive first features for an entity in the transaction data, receive second features for a benchmark set, the second features corresponding with the first features, determine an outlier value of the entity based on a Mahalanobis distance from the first features to a benchmark value representing an average for the second features. The output of the risk model can be used to prioritize review by a human data analyst. The data analyst's review of the underlying data can be used to improve the model.

Abstract translation: 计算机系统实现用于检测大量多个事务数据中的异常值的风险模型，其在一些情况下可以包含数百万或数十亿次的事务。 该计算系统包括一个非暂时的计算机可读存储介质，其存储用于由计算机处理器执行的程序指令，以便使计算系统接收交易数据中的实体的第一特征，接收用于基准集的第二特征，第二特征 对应于第一特征的特征，基于从第一特征到表示第二特征的平均值的基准值的马氏距离距离来确定实体的离群值。 风险模型的输出可用于将人力资源分析师的审查优先考虑在内。 数据分析师对底层数据的回顾可用于改进模型。

公开(公告)号：US11928733B2

公开(公告)日：2024-03-12

申请号：US17937694

申请日：2022-10-03

Applicant: Palantir Technologies Inc.

Inventor： Sean Hunter ,  Aditya Kumar ,  Jacob Albertson

IPC: G06Q40/00 ,  G06F3/0482 ,  G06F16/2457 ,  G06F16/28 ,  G06Q40/04

CPC classification number: G06Q40/04 ,  G06F3/0482 ,  G06F16/2457 ,  G06F16/285 ,  G06Q40/00

Abstract: Embodiments of the present disclosure relate to a data analysis system that may automatically generate memory-efficient clustered data structures, automatically analyze those clustered data structures, automatically tag and group those clustered data structures, and provide results of the automated analysis and grouping in an optimized way to an analyst. The automated analysis of the clustered data structures (also referred to herein as data clusters) may include an automated application of various criteria, rules, indicators, or scenarios so as to generate scores, reports, alerts, or conclusions that the analyst may quickly and efficiently use to evaluate the groups of data clusters. In particular, the groups of data clusters may be dynamically re-grouped and/or filtered in an interactive user interface so as to enable an analyst to quickly navigate among information associated with various groups of data clusters and efficiently evaluate those data clusters in the context of, for example, a risky trading investigation.

公开(公告)号：US11501369B2

公开(公告)日：2022-11-15

申请号：US16264983

申请日：2019-02-01

Applicant: Palantir Technologies Inc.

Inventor： Sean Hunter ,  Aditya Kumar ,  Jacob Albertson

IPC: G06Q40/00 ,  G06Q40/04 ,  G06F3/0482 ,  G06F16/28 ,  G06F16/2457

Abstract: Embodiments of the present disclosure relate to a data analysis system that may automatically generate memory-efficient clustered data structures, automatically analyze those clustered data structures, automatically tag and group those clustered data structures, and provide results of the automated analysis and grouping in an optimized way to an analyst. The automated analysis of the clustered data structures (also referred to herein as data clusters) may include an automated application of various criteria, rules, indicators, or scenarios so as to generate scores, reports, alerts, or conclusions that the analyst may quickly and efficiently use to evaluate the groups of data clusters. In particular, the groups of data clusters may be dynamically re-grouped and/or filtered in an interactive user interface so as to enable an analyst to quickly navigate among information associated with various groups of data clusters and efficiently evaluate those data clusters in the context of, for example, a risky trading investigation.

公开(公告)号：US09454785B1

公开(公告)日：2016-09-27

申请号：US14857071

申请日：2015-09-17

Applicant: Palantir Technologies Inc.

Inventor： Sean Hunter ,  Aditya Kumar ,  Jacob Albertson

IPC: G06Q40/00 ,  G06Q40/04 ,  G06F17/30 ,  G06F3/0482

CPC classification number: G06Q40/04 ,  G06F3/0482 ,  G06F17/30522 ,  G06F17/30598 ,  G06Q40/00

Abstract: Embodiments of the present disclosure relate to a data analysis system that may automatically generate memory-efficient clustered data structures, automatically analyze those clustered data structures, automatically tag and group those clustered data structures, and provide results of the automated analysis and grouping in an optimized way to an analyst. The automated analysis of the clustered data structures (also referred to herein as data clusters) may include an automated application of various criteria, rules, indicators, or scenarios so as to generate scores, reports, alerts, or conclusions that the analyst may quickly and efficiently use to evaluate the groups of data clusters. In particular, the groups of data clusters may be dynamically re-grouped and/or filtered in an interactive user interface so as to enable an analyst to quickly navigate among information associated with various groups of data clusters and efficiently evaluate those data clusters in the context of, for example, a risky trading investigation.

Abstract translation: 本公开的实施例涉及一种数据分析系统，其可以自动生成存储器有效的集群数据结构，自动分析这些集群数据结构，自动标记和分组这些集群数据结构，并且提供自动化分析和分组的结果。 分析师的方式。 集群数据结构（本文中也称为数据集群）的自动化分析可以包括各种标准，规则，指标或场景的自动化应用，以便生成分析师可能快速得到的分数，报告，警报或结论， 有效地用于评估数据集群。 特别地，可以在交互式用户界面中动态地重新分组和/或过滤数据集群，以便分析人员可以在与各种数据集群相关联的信息之间快速导航，并在上下文中有效地评估那些数据集群 例如，有风险的交易调查。

公开(公告)号：US20230034113A1

公开(公告)日：2023-02-02

申请号：US17937694

申请日：2022-10-03

Applicant: Palantir Technologies Inc.

Inventor： Sean Hunter ,  Aditya Kumar ,  Jacob Albertson

IPC: G06Q40/04 ,  G06F3/0482 ,  G06F16/28 ,  G06F16/2457 ,  G06Q40/00

Abstract: Embodiments of the present disclosure relate to a data analysis system that may automatically generate memory-efficient clustered data structures, automatically analyze those clustered data structures, automatically tag and group those clustered data structures, and provide results of the automated analysis and grouping in an optimized way to an analyst. The automated analysis of the clustered data structures (also referred to herein as data clusters) may include an automated application of various criteria, rules, indicators, or scenarios so as to generate scores, reports, alerts, or conclusions that the analyst may quickly and efficiently use to evaluate the groups of data clusters. In particular, the groups of data clusters may be dynamically re-grouped and/or filtered in an interactive user interface so as to enable an analyst to quickly navigate among information associated with various groups of data clusters and efficiently evaluate those data clusters in the context of, for example, a risky trading investigation.

公开(公告)号：US20190164224A1

公开(公告)日：2019-05-30

申请号：US16264983

申请日：2019-02-01

Applicant: Palantir Technologies Inc.

Inventor： Sean Hunter ,  Aditya Kumar ,  Jacob Albertson

IPC: G06Q40/04 ,  G06Q40/00 ,  G06F3/0482 ,  G06F16/2457 ,  G06F16/28

Abstract: Embodiments of the present disclosure relate to a data analysis system that may automatically generate memory-efficient clustered data structures, automatically analyze those clustered data structures, automatically tag and group those clustered data structures, and provide results of the automated analysis and grouping in an optimized way to an analyst. The automated analysis of the clustered data structures (also referred to herein as data clusters) may include an automated application of various criteria, rules, indicators, or scenarios so as to generate scores, reports, alerts, or conclusions that the analyst may quickly and efficiently use to evaluate the groups of data clusters. In particular, the groups of data clusters may be dynamically re-grouped and/or filtered in an interactive user interface so as to enable an analyst to quickly navigate among information associated with various groups of data clusters and efficiently evaluate those data clusters in the context of, for example, a risky trading investigation.

公开(公告)号：US10223748B2

公开(公告)日：2019-03-05

申请号：US15239482

申请日：2016-08-17

Applicant: Palantir Technologies Inc.

Inventor： Sean Hunter ,  Aditya Kumar ,  Jacob Albertson

IPC: G06Q40/00 ,  G06Q40/04 ,  G06F17/30 ,  G06F3/0482

Abstract: Embodiments of the present disclosure relate to a data analysis system that may automatically generate memory-efficient clustered data structures, automatically analyze those clustered data structures, automatically tag and group those clustered data structures, and provide results of the automated analysis and grouping in an optimized way to an analyst. The automated analysis of the clustered data structures (also referred to herein as data clusters) may include an automated application of various criteria, rules, indicators, or scenarios so as to generate scores, reports, alerts, or conclusions that the analyst may quickly and efficiently use to evaluate the groups of data clusters. In particular, the groups of data clusters may be dynamically re-grouped and/or filtered in an interactive user interface so as to enable an analyst to quickly navigate among information associated with various groups of data clusters and efficiently evaluate those data clusters in the context of, for example, a risky trading investigation.

公开(公告)号：US20170032463A1

公开(公告)日：2017-02-02

申请号：US15239482

申请日：2016-08-17

Applicant: Palantir Technologies Inc.

Inventor： Sean Hunter ,  Aditya Kumar ,  Jacob Albertson

IPC: G06Q40/04 ,  G06F17/30

CPC classification number: G06Q40/04 ,  G06F3/0482 ,  G06F17/30522 ,  G06F17/30598 ,  G06Q40/00

Abstract: Embodiments of the present disclosure relate to a data analysis system that may automatically generate memory-efficient clustered data structures, automatically analyze those clustered data structures, automatically tag and group those clustered data structures, and provide results of the automated analysis and grouping in an optimized way to an analyst. The automated analysis of the clustered data structures (also referred to herein as data clusters) may include an automated application of various criteria, rules, indicators, or scenarios so as to generate scores, reports, alerts, or conclusions that the analyst may quickly and efficiently use to evaluate the groups of data clusters. In particular, the groups of data clusters may be dynamically re-grouped and/or filtered in an interactive user interface so as to enable an analyst to quickly navigate among information associated with various groups of data clusters and efficiently evaluate those data clusters in the context of, for example, a risky trading investigation.

Abstract translation: 本公开的实施例涉及一种数据分析系统，其可以自动生成存储器有效的集群数据结构，自动分析这些集群数据结构，自动标记和分组这些集群数据结构，并且提供自动化分析和分组的结果。 分析师的方式。 集群数据结构（本文中也称为数据集群）的自动化分析可以包括各种标准，规则，指标或场景的自动化应用，以便生成分析师可能快速得到的分数，报告，警报或结论， 有效地用于评估数据集群。 特别地，可以在交互式用户界面中动态地重新分组和/或过滤数据集群，以便分析人员可以在与各种数据集群相关联的信息之间快速导航，并在上下文中有效地评估那些数据集群 例如，有风险的交易调查。